In Engineering Heritage Journal | Galeri Warisan Kejuruteraan (GWK)



Journal: Engineering Heritage Journal (GWK)
Author: Xiao Guang Yue , Muhammad Aqeel Ashraf

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DOI: 10.26480/gwk.01.2018.05.13

In order to predict numerical value, we propose a new intelligent algorithm opposite degree computation algorithm. The opposite degree computation algorithm is based on the degree of antagonism between the data to analyze the approximate relationship. The experiment was conducted at Chinese Xinjiang Province, during year 1995 to year 2010. Opposite degree computation algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. By learning Chinese Xinjiang cotton production data from 1995 – 2005, forecasts 2006 – 2010 cotton production; the result of the absolute error is 9.3237%. Meanwhile, we introduce the prediction method based on BP neural network for the result comparison and found opposite degree computation method is superior to the BP neural network method. Cotton production prediction based on opposite degree computation proved the algorithm is feasible and effective and can be used in numerical value prediction.
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Pages 05-13
Year 2018
Issue 1
Volume 2
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